Solving high-dimensional optimal stopping problems using deep learning
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- Yan Liu & Xiong Zhang, 2023. "Option Pricing Using LSTM: A Perspective of Realized Skewness," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
- Lukas Gonon, 2022. "Deep neural network expressivity for optimal stopping problems," Papers 2210.10443, arXiv.org.
- Philipp Grohs & Arnulf Jentzen & Diyora Salimova, 2022. "Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms," Partial Differential Equations and Applications, Springer, vol. 3(4), pages 1-41, August.
- Yuchao Dong, 2022. "Randomized Optimal Stopping Problem in Continuous time and Reinforcement Learning Algorithm," Papers 2208.02409, arXiv.org, revised Sep 2023.
- Vikranth Lokeshwar Dhandapani & Shashi Jain, 2024. "Optimizing Neural Networks for Bermudan Option Pricing: Convergence Acceleration, Future Exposure Evaluation and Interpolation in Counterparty Credit Risk," Papers 2402.15936, arXiv.org.
- Sebastian Becker & Patrick Cheridito & Arnulf Jentzen, 2020. "Pricing and Hedging American-Style Options with Deep Learning," JRFM, MDPI, vol. 13(7), pages 1-12, July.
- Christian Bayer & Denis Belomestny & Paul Hager & Paolo Pigato & John Schoenmakers, 2020. "Randomized optimal stopping algorithms and their convergence analysis," Papers 2002.00816, arXiv.org.
- A. Max Reppen & H. Mete Soner & Valentin Tissot-Daguette, 2022. "Deep Stochastic Optimization in Finance," Papers 2205.04604, arXiv.org.
- Roberto Daluiso & Emanuele Nastasi & Andrea Pallavicini & Giulio Sartorelli, 2020. "Pricing commodity swing options," Papers 2001.08906, arXiv.org.
- Xuwei Yang & Anastasis Kratsios & Florian Krach & Matheus Grasselli & Aurelien Lucchi, 2023. "Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing," Papers 2309.04557, arXiv.org.
- Ludovic Goudenege & Andrea Molent & Antonino Zanette, 2022. "Computing XVA for American basket derivatives by Machine Learning techniques," Papers 2209.06485, arXiv.org.
- Erhan Bayraktar & Qi Feng & Zhaoyu Zhang, 2022. "Deep Signature Algorithm for Multi-dimensional Path-Dependent Options," Papers 2211.11691, arXiv.org, revised Jan 2024.
- Nader Karimi & Erfan Salavati & Hirbod Assa & Hojatollah Adibi, 2023. "Sensitivity Analysis of Optimal Commodity Decision Making with Neural Networks: A Case for COVID-19," Mathematics, MDPI, vol. 11(5), pages 1-15, February.
- Laurens Van Mieghem & Antonis Papapantoleon & Jonas Papazoglou-Hennig, 2023. "Machine learning for option pricing: an empirical investigation of network architectures," Papers 2307.07657, arXiv.org.
- Beatriz Salvador & Cornelis W. Oosterlee & Remco van der Meer, 2020.
"Financial Option Valuation by Unsupervised Learning with Artificial Neural Networks,"
Mathematics, MDPI, vol. 9(1), pages 1-20, December.
- Beatriz Salvador & Cornelis W. Oosterlee & Remco van der Meer, 2020. "Financial option valuation by unsupervised learning with artificial neural networks," Papers 2005.12059, arXiv.org.
- Kentaro Hoshisashi & Yuji Yamada, 2023. "Pricing Multi-Asset Bermudan Commodity Options with Stochastic Volatility Using Neural Networks," JRFM, MDPI, vol. 16(3), pages 1-23, March.
- Bernard Lapeyre & Jérôme Lelong, 2021. "Neural network regression for Bermudan option pricing," Post-Print hal-02183587, HAL.
- A. Max Reppen & H. Mete Soner & Valentin Tissot-Daguette, 2022. "Neural Optimal Stopping Boundary," Papers 2205.04595, arXiv.org, revised May 2023.
- A. Max Reppen & H. Mete Soner & Valentin Tissot-Daguette, 2023. "Deep stochastic optimization in finance," Digital Finance, Springer, vol. 5(1), pages 91-111, March.
- Mike Ludkovski, 2020. "mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms," Papers 2012.00729, arXiv.org, revised Oct 2022.
- Beatrice Acciaio & Anastasis Kratsios & Gudmund Pammer, 2022. "Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer," Papers 2201.13094, arXiv.org, revised Mar 2023.
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